Strawberry cheesecake were the highest growth late-night food item ordered in the United States in 2020. The second highest growth late-night item was jalapeno popper.

Comprehensive dataset containing 1 verified Late night meal restaurant businesses in Indiana, United States with complete contact information, ratings, reviews, and location data.

According to a survey conducted by Rakuten Insight in Thailand, ** percent of the male respondents ordered food from food delivery apps during lunchtime as of April 2023. In contrast, ** percent of the female respondents stated that they ordered food for late-night snacks.

Comprehensive dataset of 3 Late night meal restaurants in Malaysia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 80 Late night meal restaurants in Chungcheongnam-do, South Korea as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Measurable AI UberEats E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Taiwan, Japan, Australia) - Americas (United States, Mexico, Chile) - EMEA (United Kingdom, France, Italy, United Arab Emirates, AE, South Africa)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the UberEats food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

This survey depicts the food consumed as midnight snack on an average working day in Japan as of June 2017, broken down by type of food. The survey revealed that the majority of respondents, almost ** percent, claimed to skip late-night meals during weekdays. In contrast, approximately *** percent of the Japanese respondents named food provided by home delivery services as their late-night snack of choice during weekdays.

According to the survey conducted by Rakuten Insight in Indonesia, approximately ** percent of the respondents who are aged between 25 and 34 years old stated that they preferably ordered lunch on food delivery apps. In comparison, ***** percent of respondents aged between 45 and 54 years old ordered food for late-night snacks. The same survey showed that Vietnamese usually order food ***** to *** times a week on food delivery apps.

The Quarterly Food-at-Home Price Database provides food price data to support research on the economic determinants of food consumption, diet quality, and health outcomes.

According to a 2023 survey conducted by Rakuten Insight in South Korea, around **** percent of respondents stated that they were ordering dinner from food delivery apps. This was by far the most popular mealtime to order food, followed by lunch and late-night snacks.

Graph and download economic data for All Employees, Food Services and Drinking Places (CEU7072200001) from Jan 1990 to Jul 2025 about leisure, hospitality, establishment survey, food, services, employment, and USA.

The Delta Food Outlets Study was an observational study designed to assess the nutritional environments of 5 towns located in the Lower Mississippi Delta region of Mississippi. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns in which Delta Healthy Sprouts participants resided and that contained at least one convenience (corner) store, grocery store, or gas station. Data were collected via electronic surveys between March 2016 and September 2018 using the Nutrition Environment Measures Survey (NEMS) tools. Survey scores for the NEMS Corner Store, NEMS Grocery Store, and NEMS Restaurant were computed using modified scoring algorithms provided for these tools via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one (NEMS-C) contains data collected with the NEMS Corner (convenience) Store tool. Dataset two (NEMS-G) contains data collected with the NEMS Grocery Store tool. Dataset three (NEMS-R) contains data collected with the NEMS Restaurant tool. Resources in this dataset:Resource Title: Delta Food Outlets Data Dictionary. File Name: DFO_DataDictionary_Public.csvResource Description: This file contains the data dictionary for all 3 datasets that are part of the Delta Food Outlets Study.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One NEMS-C. File Name: NEMS-C Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for convenience stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two NEMS-G. File Name: NEMS-G Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for grocery stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three NEMS-R. File Name: NEMS-R Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for restaurants.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The Allegheny County Health Department has generated this list of fast food restaurants by exporting all chain restaurants without an alcohol permit from the County’s Fee and Permit System. A chain restaurant defined by the County is any restaurant that has more than one location in the County. Chain restaurants capture both local and national chains (including locally owned national chains) so long as there is one or more establishments in operation within the County.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the downloads tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

With some exceptions, all samples are probability based and nationally representative of the resident adult population. The coverage area is the entire country including rural areas, and the sampling frame represents the entire civilian, non-institutionalized, aged 15 and older population. For more details on the overall sampling and data collection methodology, see the World poll methodology attached as a resource in the downloads tab. Specific sampling details for each country are also attached as technical documents in the downloads tab. Exclusions: Transnistria (Prednestrovie) excluded for safety of interviewers. The excluded area represents approximately 13% of the population. Design effect: 1.97

Mode of data collection

Face-to-Face [f2f]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 4.4. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

United States CPI U: FB: Food: Food at Home (Home) data was reported at 239.158 1982-1984=100 in Jun 2018. This records a decrease from the previous number of 239.287 1982-1984=100 for May 2018. United States CPI U: FB: Food: Food at Home (Home) data is updated monthly, averaging 98.400 1982-1984=100 from Jan 1947 (Median) to Jun 2018, with 858 observations. The data reached an all-time high of 243.779 1982-1984=100 in Oct 2015 and a record low of 24.300 1982-1984=100 in Feb 1947. United States CPI U: FB: Food: Food at Home (Home) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I002: Consumer Price Index: Urban.

MyPyramid Food Data provides information on the total calories; calories from solid fats, added sugars, and alcohol (extras); MyPyramid food group and subgroup amounts; and saturated fat content of over 1,000 commonly eaten foods with corresponding commonly used portion amounts. This information is key to help consumers meet the recommendations of the Dietary Guidelines for Americans and manage their weight by understanding how many calories are consumed from "extras." CNPP has created an interactive tool from this data set available on the web at MyFood-a-pedia.gov. A mobile version is coming soon to provide consumers with assistance on-the-go.

Snapshot img

US Fast Casual Restaurants Market Size 2025-2029

The US fast casual restaurants market size is forecast to increase by USD 84.5 billion, at a CAGR of 13.7% between 2024 and 2029.

The fast casual restaurants market is experiencing continuous evolution driven by growing consumer demand for innovation and menu customization. The increasing preference for personalized dining experiences is encouraging operators to develop diverse offerings that address a range of tastes and dietary requirements. This dynamic is intensifying competitive activity, with brands focusing on distinctive culinary concepts to strengthen positioning. Digital transformation remains a pivotal trend, with contactless ordering, mobile applications, and integrated online platforms becoming indispensable in meeting shifting customer expectations and enhancing operational efficiency.
Within the latest market data, a notable comparison highlights that the adoption rate of mobile-based ordering has expanded faster than in-store ordering uptake, reflecting the influence of resturant technology-led convenience on consumer choices. Simultaneously, digital payment transactions have grown at a rate exceeding that of traditional payment methods, underlining the shift toward seamless, tech-enabled service models.
A core challenge persists in the form of strong competition from quick-service formats offering similar convenience at competitive price points. To sustain relevance, fast casual restaurants are placing strategic emphasis on superior food quality, differentiated dining experiences, and targeted marketing approaches. By aligning innovation with advanced service integration, these businesses can maintain a distinct advantage in an increasingly saturated market landscape.

Major Market Trends & Insights

  By the Channel, the Dine-in sub-segment was valued at USD 48.90 billion in 2022
  By the Application, the Franchised sub-segment accounted for the largest market revenue share in 2022

Market Size & Forecast

  Market Opportunities: USD 148.4 billion
  Future Opportunities: USD 84.5 billion 
  CAGR : 13.7%

What will be the size of the US Fast Casual Restaurants Market during the forecast period?

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US fast casual restaurant market is undergoing steady transformation as operators adapt to shifting consumer expectations and operational challenges. The adoption of third-party delivery fees has altered average order values while impacting profitability, prompting greater emphasis on menu item profitability and marketing campaign ROI to sustain revenue growth. Employee retention programs, food safety compliance, and customer service training remain central to maintaining high satisfaction levels and repeat customer rate, supported by online reputation management strategies that strengthen brand perception. Energy consumption tracking, peak demand forecasting, and unit level profitability assessment are being leveraged to enhance efficiency, alongside supply chain visibility and kitchen workflow efficiency improvements.
A notable data comparison shows that while the repeat customer rate has grown by 12%, labor productivity metrics have only improved by 8% over the same period, highlighting a gap between customer loyalty growth and operational output gains. Similarly, waste reduction programs have reduced food cost percentage by 4%, compared to a 6% decrease achieved through ingredient cost control, indicating stronger returns from sourcing strategies.
Future market expectations remain positive, with growth projected at over 5% annually, driven by ingredient sourcing strategy optimization, labor productivity enhancement, and social media reach expansion. As restaurants refine inventory management through peak demand forecasting and improve unit level profitability, the alignment of operational efficiency with evolving dining preferences will remain the foundation of long-term competitiveness.

How is this US Fast Casual Restaurants market segmented?

The US fast casual restaurants market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Channel

  Dine-in
  Takeaway


Application

  Franchised
  Standalone


Food Type

  Burger/Sandwich
  Pizza/Pasta
  Asian
  Latin American
  Chicken
  Others


Target Audience

  Millennials
  Working Professionals
  Families


Distribution Channel Specificity

  Specialty Chains
  Online Platforms
  Retail Foodservice


Geography

  North America

    US

By Channel Insights

The dine-in segment is estimated to witness significant was valued at USD 48.90 billion in 2019 and showed a gradual increase during the forecast period.

Fast casual restaurants, primarily found in the US and Cana

According to the survey conducted by Rakuten Insight, approximately ** percent of the female and ** percent of male respondents stated that they preferably ordered lunch on food delivery apps in Vietnam. In comparison, ** percent of female and ** percent of male respondents ordered food for late-night snacks. The same survey showed that Vietnamese usually ordered ***** to *** times a week.

Graph and download economic data for All Employees, Food and Beverage Retailers (CES4244500001) from Jan 1990 to Jul 2025 about beverages, establishment survey, retail trade, food, sales, retail, employment, and USA.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Food at Home in U.S. City Average (CUUS0000SAF11) from H1 1984 to H1 2025 about food, urban, consumer, CPI, housing, price index, indexes, price, and USA.